Abstract
Evolving behaviours by spammers on online social networks continue to be a big challenge; this phenomenon has consistently received attention from researchers in terms of how it can be combated. On micro-blogging communities, such as Twitter, spammers intentionally change their behavioral patterns and message contents to avoid detection. Many existing approaches have been proposed but are limited due to the characterization of spammers’ behaviour with unified features, without considering the fact that spammers behave differently, and this results in distinct patterns and features. In this study, we approach the challenge of spammer detection by utilizing the level of focused interest patterns of users. We propose quantity methods to measure the change in user’s interest and determine whether the user has a focused-interest or a diverse-interest. Then we represent users by features based on the level of focused interest. We develop a framework by combining unsupervised and supervised learning to differentiate between spammers and legitimate users. The results of this experiment show that our proposed approach can effectively differentiate between spammers and legitimate users regarding the level of focused interest. To the best of our knowledge, our study is the first to provide a generic and efficient framework to represent user-focused interest level that can handle the problem of the evolving behaviour of spammers.
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Alghamdi, B., Xu, Y., Watson, J. (2018). A Hybrid Approach for Detecting Spammers in Online Social Networks. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11233. Springer, Cham. https://doi.org/10.1007/978-3-030-02922-7_13
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DOI: https://doi.org/10.1007/978-3-030-02922-7_13
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